10x Chick Neural Tube

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This analysis is ran using Seurat v3.1. For more information see https://satijalab.org/seurat/

Set environmental variables, load data and make Seurat object

In order to be able to run the script from either Rstudio, local terminal, or cluster terminal, I add a switch which looks for command line arguments. This then sets the directory paths accordingly.

#!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)

if (length(args)==0) {
  cat('no arguments provided\n')
  
  sapply(list.files('/Users/alex/dev/repos/10x_neural_tube/R/my_functions/', full.names = T), source)
  
  plot.path = "/Users/alex/dev/output/10x_neural_tube/plots/1_seurat_full/"
  rds.path = "/Users/alex/dev/output/10x_neural_tube/RDS.files/1_seurat_full/"
  dir.create(plot.path, recursive = T)
  dir.create(rds.path, recursive = T)
  
  ###### load data ##########
  sample.paths<-data.frame(tissue = c("hh4", "hh6", "ss4", "ss8"),
                           path = c("/Users/alex/dev/data/10x/cellranger_output/cellranger_count_hh4/outs/filtered_feature_bc_matrix_chr_edit/",
                                    "/Users/alex/dev/data/10x/cellranger_output/cellranger_count_hh6/outs/filtered_feature_bc_matrix_chr_edit/",
                                    "/Users/alex/dev/data/10x/cellranger_output/cellranger_count_4ss/outs/filtered_feature_bc_matrix_chr_edit/",
                                    "/Users/alex/dev/data/10x/cellranger_output/cellranger_count_8ss/outs/filtered_feature_bc_matrix_chr_edit/"))
} else if (length(args)==1) {
  if (args[1] == "CAMP") {
    cat('data loaded from CAMP\n')
    
    project.dir = "~/working/alexthiery/analysis/10x_scRNAseq_2019/"
    
    sapply(list.files(paste0(project.dir, 'repo/scripts/my_functions/'), full.names = T), source)

    plot.path = paste0(project.dir, "output/plots/1_seurat_full/")
    rds.path = paste0(project.dir, "output/RDS.files/1_seurat_full/")
    dir.create(plot.path, recursive = T)
    dir.create(rds.path, recursive = T)

    input.dat = paste0(project.dir, "cellranger_output/")

    ###### load data ##########
    sample.paths<-data.frame(tissue = c("hh4", "hh6", "ss4", "ss8"),
                             path = c('~/working/alexthiery/analysis/10x_scRNAseq_2019/cellranger_ouput/hh4_filtered_feature_bc_matrix_chr_edit/',
                                      '~/working/alexthiery/analysis/10x_scRNAseq_2019/cellranger_ouput/hh6_filtered_feature_bc_matrix_chr_edit/',
                                      '~/working/alexthiery/analysis/10x_scRNAseq_2019/cellranger_ouput/4ss_filtered_feature_bc_matrix_chr_edit/',
                                      '~/working/alexthiery/analysis/10x_scRNAseq_2019/cellranger_ouput/8ss_filtered_feature_bc_matrix_chr_edit/'))

  } else {stop("Only CAMP can be supplied as arguments")}
} else {stop("only one argument can be supplied")}

Load packages - packages are stored within renv in the repository

library(dplyr)
library(Antler)
library(Seurat)
library(cowplot)
library(clustree)
library(gridExtra)
library(grid)
library(pheatmap)

Make Seurat objects for each of the different samples.

for(i in 1:nrow(sample.paths["path"])){
  name<-paste(sample.paths[i,"tissue"])
  assign(name, CreateSeuratObject(counts= Read10X(data.dir = paste(sample.paths[i,"path"])), project = paste(sample.paths[i, "tissue"])))
}

The four Seurat objects are then merged, before running CreateSeuratObject again on the output in order to apply the min.cells parameter on the final merged dataset.

temp <- merge(hh4, y = c(hh6, ss4, ss8), add.cell.ids = c("hh4", "hh6", "ss4", "ss8"), project = "chick.10x")
merged.data<-CreateSeuratObject(GetAssayData(temp), min.cells = 3, project = "chick.10x.mincells3")

The original Seurat objects are then removed from the global environment

rm(hh4, hh6, ss4, ss8, sample.paths, temp)

Store mitochondrial percentage in object meta data

merged.data <- PercentageFeatureSet(merged.data, pattern = "^MT-", col.name = "percent.mt")

Filter data based on variable threshold

Remove data which do not pass filter threshold

merged.data <- subset(merged.data, subset = c(nFeature_RNA > 1000 & nFeature_RNA < 6000 & percent.mt < 15))

Log normalize data and find variable features

norm.data <- NormalizeData(merged.data, normalization.method = "LogNormalize", scale.factor = 10000)
norm.data <- FindVariableFeatures(norm.data, selection.method = "vst", nfeatures = 2000)

Scale data and regress out MT content

norm.data <- ScaleData(norm.data, features = rownames(norm.data), vars.to.regress = "percent.mt")

Save RDS after scaling as this step takes time

saveRDS(norm.data, paste0(rds.path, "norm.data.RDS"))

Perform dimensionality reduction by PCA and UMAP embedding

Read in RDS data if needed

# norm.data <- readRDS(paste0(rds.path, "norm.data.RDS"))

Change plot path

curr.plot.path <- paste0(plot.path, '0_filt_data/')
dir.create(curr.plot.path)

Run PCA analysis on the each set of data

norm.data <- RunPCA(object = norm.data, verbose = FALSE)

PCA

Seurat’s clustering algorithm is based on principle components, so we need to ensure that only the informative PCs are kept!

Dimensions heatmap

Plot heatmap of top variable genes across top principle components

pdf(paste0(curr.plot.path, "dimHM.pdf"),width=15,height=25)
DimHeatmap(norm.data, dims = 1:30, balanced = TRUE, cells = 500)
graphics.off()

Alt text

ElbowPlot

Another heuristic method is ElbowPlot which ranks PCs based on the % variance explained by each PC

pdf(paste0(curr.plot.path, "elbowplot.pdf"),width=12,height=10)
print(ElbowPlot(norm.data, ndims = 40))
graphics.off()

PCA level comparison

Run clustering and UMAP at different PCA cutoffs - save this output to compare the optimal number of PCs to be used

pdf(paste0(curr.plot.path, 'UMAP_PCA_comparison.pdf'), width= 20, height= 15)
PCA.level.comparison(norm.data, PCA.levels = c(7, 10, 15, 20), cluster_res = 0.5)
graphics.off()

Clustering and UMAP

Use PCA=15 as elbow plot is relatively stable across stages Use clustering resolution = 0.5 for filtering

norm.data <- FindNeighbors(norm.data, dims = 1:15, verbose = FALSE)
norm.data <- RunUMAP(norm.data, dims = 1:15, verbose = FALSE)
norm.data <- FindClusters(norm.data, resolution = 0.5, verbose = FALSE)

UMAP

Plot UMAP for clusters and developmental stage

pdf(paste0(curr.plot.path, "UMAP.pdf"), width=10, height=5)
clust.stage.plot(norm.data)
graphics.off()

Cluster QC

Plot QC for each cluster

pdf(paste0(curr.plot.path, "cluster.QC.pdf"), height = 7, width = 18)
QC.plot(norm.data)
graphics.off()

DE heatmap

Find differentially expressed genes and plot heatmap of top 15 DE genes for each cluster

markers <- FindAllMarkers(norm.data, only.pos = T, logfc.threshold = 0.25)
top15 <- markers %>% group_by(cluster) %>% top_n(n = 15, wt = avg_logFC)

png(paste0(curr.plot.path, "HM.top15.DE.png"), width=75, height=50, units = "cm", res = 200)
tenx.pheatmap(data = norm.data, metadata = c("orig.ident", "seurat_clusters"), used.genes = unique(top15$gene), main = "")
graphics.off()

Heatmap clearly shows clusters segregate by sex - check this and remove sex genes

---

Calculating sex effect and removing sex genes

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Change plot path

curr.plot.path <- paste0(plot.path, '1_sex_filt/')
dir.create(curr.plot.path)

There is a strong sex effect - this plot shows DE genes between clusters 1 and 2 which are hh4 clusters. Clustering is driven by sex genes

png(paste0(curr.plot.path, "HM.top15.DE.png"), width=70, height=40, units = "cm", res = 200)
tenx.pheatmap(data = norm.data[,rownames(norm.data@meta.data[norm.data$seurat_clusters == 1 | norm.data$seurat_clusters == 2,])],
              metadata = c("orig.ident", "seurat_clusters"), used.genes = rownames(FindMarkers(norm.data, ident.1 = 1, ident.2 = 2)),
              main = "", hclust_rows = T)
graphics.off()

Use W chromosome genes to K-means cluster the cells into male (zz) and female (zw)

W_genes <- as.matrix(norm.data@assays$RNA[grepl("W-", rownames(norm.data@assays$RNA)),])
k_clusters <- kmeans(t(W_genes), 2)
k_clusters <- data.frame(k_clusters$cluster)
norm.data@meta.data$k_clusters <- k_clusters[match(colnames(norm.data@assays$RNA), rownames(k_clusters)),]

Get rownames for kmeans clusters 1 and 2

k_clus_1 <- rownames(norm.data@meta.data[norm.data@meta.data$k_clusters == 1,])
k_clus_2 <- rownames(norm.data@meta.data[norm.data@meta.data$k_clusters == 2,])

K clustering identities are stochastic, so I need to identify which cluster is male and female Sum of W genes is order of magnitude greater in cluster 2 - these are the female cells

sumclus1 <- sum(W_genes[,k_clus_1])
sumclus2 <- sum(W_genes[,k_clus_2])

if(sumclus1 < sumclus2){
  k.male <- k_clus_1
  k.female <- k_clus_2
} else {
  k.female <- k_clus_1
  k.male <- k_clus_2
}

cell.sex.ID <- list("male.cells" = k.male, "female.cells" = k.female)
saveRDS(cell.sex.ID, paste0(rds.path, "sex_kmeans.RDS"))

Add sex data to meta.data

norm.data@meta.data$sex <- unlist(lapply(rownames(norm.data@meta.data), function(x)
  if(x %in% k.male){"male"} else if(x %in% k.female){"female"} else{stop("cell sex is not assigned")}))

Calculating sex effect

Next - subset autosomal genes and Z genes - then calculate the average for each gene for both kmeans clustered cells and plot in order to compare whether the two groups show significant differences in their expression.

This is a test on autosomal genes to try and calculate and compare FC betweeen clusters.

Calculating median is tricky as there are a lot of dropouts in 10x data so you end up with either 0s (when the median = 0) or 1 (when the median expression in both clusters is the same - probably a result of normalisation resulting in a UMI of 0 or 1 being normalised to a nominal value)

Make dataframe for mean Z expression in male cells

mean.Z.male <- data.frame(Z.mean = apply(norm.data@assays$RNA[grepl("Z-", rownames(norm.data@assays$RNA)), k.male], 1, mean))

Add 1 before log2 as log2(1) = 0

mean.Z.male <- log2(mean.Z.male + 1)

Make dataframe for mean Z expression in female cells

mean.Z.female <- data.frame(Z.mean = apply(norm.data@assays$RNA[grepl("Z-", rownames(norm.data@assays$RNA)), k.female], 1, mean))
mean.Z.female <- log2(mean.Z.female + 1)

Make dataframe for mean autosomal expression in male cells

mean.auto.male <- data.frame(auto.mean = apply(norm.data@assays$RNA[!grepl("Z-", rownames(norm.data@assays$RNA)) & !grepl("W-", rownames(norm.data@assays$RNA)), k.male], 1, mean))
mean.auto.male <- log2(mean.auto.male + 1)

Make dataframe for mean autosomal expression in male cells

mean.auto.female <- data.frame(auto.mean = apply(norm.data@assays$RNA[!grepl("Z-", rownames(norm.data@assays$RNA)) & !grepl("W-", rownames(norm.data@assays$RNA)), k.female], 1, mean))
mean.auto.female <- log2(mean.auto.female + 1)

Calculate FC by subtracting log2 expression from each other

FC <- list()
FC$Z <- mean.Z.male - mean.Z.female
FC$auto <-  mean.auto.male - mean.auto.female

Plot boxplot of Z gene and autosomal expression in male vs female cells

pdf(paste0(curr.plot.path,"sex_kmeans_log2FC_boxplot.pdf"))
boxplot(c(FC$Z, FC$auto),  ylab = "male - female log2 FC (mean normalised UMI +1)", names = c("Z chromosome genes", "autosomal genes"))
graphics.off()

Z genes are upregulated within male genes relative to female genes whereas autosomal genes have a normal distribution of logFC therefore Z genes should be filtered out

Filter sex genes

Following subsetting of cells and/or genes, the same pipeline as above is repeated i.e. Find variable features -> Scale data/regress out confounding variables -> PCA -> Find neighbours -> Run UMAP -> Find Clusters -> Cluster QC -> Find top DE genes

Remove sex genes

norm.data.sexfilt <- norm.data[rownames(norm.data)[!grepl("W-", rownames(norm.data)) & !grepl("Z-", rownames(norm.data))], ]

Re-run findvariablefeatures and scaling

norm.data.sexfilt <- FindVariableFeatures(norm.data.sexfilt, selection.method = "vst", nfeatures = 2000)
norm.data.sexfilt <- ScaleData(norm.data.sexfilt, features = rownames(norm.data.sexfilt), vars.to.regress = c("percent.mt", "sex"))

Save RDS

saveRDS(norm.data.sexfilt, paste0(rds.path, "norm.data.sexfilt.RDS"))

Read in RDS data if needed

# norm.data.sexfilt <- readRDS(paste0(rds.path, "norm.data.sexfilt.RDS"))

Set plot path

curr.plot.path <- paste0(plot.path, '1_sex_filt/')

PCA

norm.data.sexfilt <- RunPCA(object = norm.data.sexfilt, verbose = FALSE)

pdf(paste0(curr.plot.path, "dimHM.pdf"),width=15,height=25)
DimHeatmap(norm.data.sexfilt, dims = 1:30, balanced = TRUE, cells = 500)
graphics.off()

pdf(paste0(curr.plot.path, "elbowplot.pdf"),width=12,height=10)
print(ElbowPlot(norm.data.sexfilt, ndims = 40))
graphics.off()

pdf(paste0(curr.plot.path, 'UMAP_PCA_comparison.pdf'), width= 20, height= 15)
PCA.level.comparison(norm.data.sexfilt, PCA.levels = c(7, 10, 15, 20), cluster_res = 0.5)
graphics.off()

Dimensions heatmap

ElbowPlot

PCA level comparison

Clustering and UMAP

Use PCA=15 as elbow plot is relatively stable across stages

norm.data.sexfilt <- FindNeighbors(norm.data.sexfilt, dims = 1:15, verbose = FALSE)
norm.data.sexfilt <- RunUMAP(norm.data.sexfilt, dims = 1:15, verbose = FALSE)

Find optimal cluster resolution

pdf(paste0(curr.plot.path, "clustree.pdf"), width= 25, height= 15, onefile = F)
clust.res(seurat.obj = norm.data.sexfilt, by = 0.2)
graphics.off()

Use clustering resolution = 1.4 for subsequent filtering of poor quality clusters this increases the stringency of poor quality clusters, removing the least data possible

norm.data.sexfilt <- FindClusters(norm.data.sexfilt, resolution = 1.4, verbose = FALSE)

UMAP

Plot UMAP for clusters and developmental stage

pdf(paste0(curr.plot.path, "UMAP.pdf"), width=10, height=5)
clust.stage.plot(norm.data.sexfilt)
graphics.off()

Cluster QC

Plot QC for each cluster

pdf(paste0(curr.plot.path, "cluster.QC.pdf"), height = 7, width = 18)
QC.plot(norm.data.sexfilt)
graphics.off()

DE heatmap

Find differentially expressed genes and plot heatmap of top 15 DE genes for each cluster

markers <- FindAllMarkers(norm.data.sexfilt, only.pos = T, logfc.threshold = 0.25)
top15 <- markers %>% group_by(cluster) %>% top_n(n = 15, wt = avg_logFC)

png(paste0(curr.plot.path, "HM.top15.DE.png"), width=75, height=50, units = "cm", res = 200)
tenx.pheatmap(data = norm.data.sexfilt, metadata = c("orig.ident", "seurat_clusters"), used.genes = unique(top15$gene), main = "")
graphics.off()

---

Identify and remove poor quality clusters / contamination

---

Change plot path

curr.plot.path <- paste0(plot.path, "2_cluster_filt/")
dir.create(curr.plot.path)

Feature plots

Identify mesoderm and PGCs using candidate genes

genes <- c("EYA2", "SIX1", "TWIST1", "PITX2", "SOX17", "DAZL", "DND1", "CXCR4")

ncol = 3
pdf(paste0(curr.plot.path, "UMAP_GOI.pdf"), width = ncol*4, height = 5*ceiling(length(genes)/ncol))
multi.feature.plot(seurat.obj = norm.data.sexfilt, gene.list = genes, plot.clusters = T,
                   plot.stage = T, label = "", cluster.col = "RNA_snn_res.1.4", n.col = ncol)
graphics.off()

DotPlots

Dotplot for identifying PGCs, Early mesoderm and Late mesoderm

pdf(paste0(curr.plot.path, "dotplot.GOI.pdf"), height = 8, width = 10)
DotPlot(norm.data.sexfilt, features = c( "SOX17", "CXCR4","EYA2", "TWIST1", "SIX1",  "PITX2", "DAZL"))
graphics.off()

Remove contaminating cells from clusters

Clust 12, 14, 17 = poor quality cells Clust 18 = early mesoderm - expresses sox17, eya2, pitx2, cxcr4 Clust 20 = Late mesoderm - expresses twist1, six1, eya2 Clust 21 = PGC’s - expresses dazl very highly

norm.data.clustfilt <- rownames(norm.data.sexfilt@meta.data)[norm.data.sexfilt@meta.data$seurat_clusters == 12 |
                                                               norm.data.sexfilt@meta.data$seurat_clusters == 14 |
                                                               norm.data.sexfilt@meta.data$seurat_clusters == 17 |
                                                               norm.data.sexfilt@meta.data$seurat_clusters == 18 |
                                                               norm.data.sexfilt@meta.data$seurat_clusters == 20 |
                                                               norm.data.sexfilt@meta.data$seurat_clusters == 21]

norm.data.clustfilt <- subset(norm.data.sexfilt, cells = norm.data.clustfilt, invert = T)

Re-run findvariablefeatures and scaling

norm.data.clustfilt <- FindVariableFeatures(norm.data.clustfilt, selection.method = "vst", nfeatures = 2000)
norm.data.clustfilt <- ScaleData(norm.data.clustfilt, features = rownames(norm.data.clustfilt), vars.to.regress = c("percent.mt", "sex"))

saveRDS(norm.data.clustfilt, paste0(rds.path, "norm.data.clustfilt.RDS"))

Read in RDS data if needed

# norm.data.clustfilt <- readRDS(paste0(rds.path, "norm.data.clustfilt.RDS"))

PCA

norm.data.clustfilt <- RunPCA(object = norm.data.clustfilt, verbose = FALSE)

pdf(paste0(curr.plot.path, "dimHM.pdf"),width=15,height=25)
DimHeatmap(norm.data.clustfilt, dims = 1:30, balanced = TRUE, cells = 500)
graphics.off()

pdf(paste0(curr.plot.path, "elbowplot.pdf"),width=12,height=10)
print(ElbowPlot(norm.data.clustfilt, ndims = 40))
graphics.off()

pdf(paste0(curr.plot.path, 'UMAP_PCA_comparison.pdf'), width= 20, height= 15)
PCA.level.comparison(norm.data.clustfilt, PCA.levels = c(7, 10, 15, 20), cluster_res = 0.5)
graphics.off()

Dimensions heatmap

ElbowPlot

PCA level comparison

Clustering and UMAP

Use PCA=15 as elbow plot is relatively stable across stages

norm.data.clustfilt <- FindNeighbors(norm.data.clustfilt, dims = 1:15, verbose = FALSE)
norm.data.clustfilt <- RunUMAP(norm.data.clustfilt, dims = 1:15, verbose = FALSE)

Find optimal cluster resolution

pdf(paste0(curr.plot.path, "clustree.pdf"), width= 25, height= 15, onefile = F)
clust.res(seurat.obj = norm.data.clustfilt, by = 0.2)
graphics.off()

Use clustering resolution = 0.8

norm.data.clustfilt <- FindClusters(norm.data.clustfilt, resolution = 0.8)

UMAP

Plot UMAP for clusters and developmental stage

pdf(paste0(curr.plot.path, "UMAP.pdf"), width=10, height=5)
clust.stage.plot(norm.data.clustfilt)
graphics.off()

---

Check for cell cycle effect

---

Set plot path

curr.plot.path <- paste0(plot.path, "3_cell_cycle/")
dir.create(curr.plot.path)

Calculate cell cycle for each cell

s.genes <- cc.genes$s.genes
g2m.genes <- cc.genes$g2m.genes
pre.cell.cycle.dat <- CellCycleScoring(norm.data.clustfilt, s.features = s.genes, g2m.features = g2m.genes, set.ident = TRUE)

Re-run findvariablefeatures and scaling

norm.data.clustfilt.cc <- FindVariableFeatures(pre.cell.cycle.dat, selection.method = "vst", nfeatures = 2000)
norm.data.clustfilt.cc <- ScaleData(norm.data.clustfilt.cc, features = rownames(norm.data.clustfilt.cc), vars.to.regress = c("percent.mt", "sex", "S.Score", "G2M.Score"))

saveRDS(norm.data.clustfilt.cc, paste0(rds.path, "norm.data.clustfilt.cc.RDS"))

Read in RDS data if needed

# norm.data.clustfilt.cc <- readRDS(paste0(rds.path, "norm.data.clustfilt.cc.RDS"))

PCA

norm.data.clustfilt.cc <- RunPCA(object = norm.data.clustfilt.cc, verbose = FALSE)

pdf(paste0(curr.plot.path, "dimHM.pdf"),width=15,height=25)
DimHeatmap(norm.data.clustfilt.cc, dims = 1:30, balanced = TRUE, cells = 500)
graphics.off()

pdf(paste0(curr.plot.path, "elbowplot.pdf"),width=12,height=10)
print(ElbowPlot(norm.data.clustfilt.cc, ndims = 40))
graphics.off()

pdf(paste0(curr.plot.path, 'UMAP_PCA_comparison.pdf'), width= 20, height= 15)
PCA.level.comparison(norm.data.clustfilt.cc, PCA.levels = c(7, 10, 15, 20), cluster_res = 0.5)
graphics.off()

Dimensions heatmap

ElbowPlot

PCA level comparison

Clustering and UMAP

Use PCA=15 as elbow plot is relatively stable across stages

norm.data.clustfilt.cc <- FindNeighbors(norm.data.clustfilt.cc, dims = 1:15, verbose = FALSE)
norm.data.clustfilt.cc <- RunUMAP(norm.data.clustfilt.cc, dims = 1:15, verbose = FALSE)

Find optimal cluster resolution

pdf(paste0(curr.plot.path, "clustree.pdf"), width= 25, height= 15, onefile = F)
clust.res(seurat.obj = norm.data.clustfilt.cc, by = 0.2)
graphics.off()

Use clustering resolution = 1.2

norm.data.clustfilt.cc <- FindClusters(norm.data.clustfilt.cc, resolution = 1.2)

Cell cycle UMAP

UMAP of cell cycle before and after regressing out

pdf(paste0(curr.plot.path, "cell.cycle.pdf"), width = 14, height = 7)
pre.plot <- DimPlot(pre.cell.cycle.dat, group.by = "Phase", reduction = "umap")
post.plot <- DimPlot(norm.data.clustfilt.cc, group.by = "Phase", reduction = "umap")
print(gridExtra::grid.arrange(pre.plot, post.plot, ncol = 2))
graphics.off()

UMAP

Plot UMAP for clusters and developmental stage

pdf(paste0(curr.plot.path, "UMAP.pdf"), width=10, height=5)
clust.stage.plot(norm.data.clustfilt.cc)
graphics.off()

Cluster QC

Plot QC for each cluster

pdf(paste0(curr.plot.path, "cluster.QC.pdf"), height = 7, width = 18)
QC.plot(norm.data.clustfilt.cc)
graphics.off()

DE heatmap

Find differentially expressed genes and plot heatmap of top DE genes for each cluster

markers <- FindAllMarkers(norm.data.clustfilt.cc, only.pos = T, logfc.threshold = 0.25)
# Re-order genes in top15 based on desired cluster order in subsequent plot - this orders them in the heatmap in the correct order
top15 <- markers %>% group_by(cluster) %>% top_n(n = 15, wt = avg_logFC) %>% arrange(factor(cluster, levels = c(1,2,11,7,0,6,4,8,9,10,3,5,12,13,14)))

png(paste0(curr.plot.path, "HM.top15.DE.png"), width=100, height=75, units = "cm", res = 200)
tenx.pheatmap(data = norm.data.clustfilt.cc, metadata = c("orig.ident", "seurat_clusters"), used.genes = unique(top15$gene),
              main = "", order.by = "seurat_clusters", custom_order = c(1,2,11,7,0,6,4,8,9,10,3,5,12,13,14))
graphics.off()

---

Cell type identification

---

Set plot path

curr.plot.path <- paste0(plot.path, "4_stage_split/")
dir.create(curr.plot.path)

Split dataset into different stages

seurat_stage <- lapply(c('hh4', 'hh6', 'ss4', 'ss8'),
       function(x) subset(norm.data.clustfilt.cc, cells = rownames(norm.data.clustfilt.cc@meta.data)[norm.data.clustfilt.cc$orig.ident == x]))
names(seurat_stage) = c('hh4', 'hh6', 'ss4', 'ss8')

Re-run findvariablefeatures and scaling

seurat_stage <- lapply(seurat_stage, function(x) FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000))
seurat_stage <- lapply(seurat_stage, function(x) ScaleData(x, features = rownames(norm.data.clustfilt.cc),
                                                           vars.to.regress = c("percent.mt", "sex", "S.Score", "G2M.Score")))

saveRDS(seurat_stage, paste0(rds.path, "seurat_stage.RDS"))

Read in RDS data if needed

# seurat_stage <- readRDS(paste0(rds.path, "seurat_stage.RDS"))

PCA

seurat_stage <- lapply(seurat_stage, function(x) RunPCA(object = x, verbose = FALSE))

for(stage in names(seurat_stage)){
  pdf(paste0(curr.plot.path, "dimHM.", stage, ".pdf"),width=15,height=25)
  DimHeatmap(seurat_stage[[stage]], dims = 1:30, balanced = TRUE, cells = 500)
  graphics.off()
  
  pdf(paste0(curr.plot.path, "elbowplot.", stage, ".pdf"),width=12,height=10)
  print(ElbowPlot(seurat_stage[[stage]], ndims = 40))
  graphics.off()
  
  pdf(paste0(curr.plot.path, "UMAP_PCA_comparison.", stage, ".pdf"), width= 20, height= 15)
  PCA.level.comparison(seurat_stage[[stage]], PCA.levels = c(7, 10, 15, 20), cluster_res = 0.5)
  graphics.off()
}

Dimensions heatmap

HH4

HH6

4ss

8ss

ElbowPlot

HH4

HH6

4ss

8ss

PCA level comparison

HH4

HH6

4ss

8ss

Clustering and UMAP

Use PCA=15 as elbow plot is relatively stable across stages

seurat_stage <- lapply(seurat_stage, function(x) FindNeighbors(x, dims = 1:20, verbose = FALSE))
seurat_stage <- lapply(seurat_stage, function(x) RunUMAP(x, dims = 1:20, verbose = FALSE))

Find optimal cluster resolution

for(stage in names(seurat_stage)){
  pdf(paste0(curr.plot.path, "clustree.", stage, ".pdf"), width= 25, height= 15, onefile = F)
  clust.res(seurat.obj = seurat_stage[[stage]], by = 0.1)
  graphics.off()
}

HH4

HH6

4ss

8ss

Use custom clustering resolution for each stage

res = c("hh4" = 0.5, "hh6" = 0.8, "ss4" = 0.3, "ss8" = 0.3)
seurat_stage <- lapply(names(res), function(x) FindClusters(seurat_stage[[x]], resolution = res[names(res) %in% x]))
names(seurat_stage) = names(res)

Plots

UMAP

Plot UMAP for clusters and developmental stage

for(stage in names(seurat_stage)){
  pdf(paste0(curr.plot.path, "UMAP.", stage, ".pdf"), width=10, height=5)
  clust.stage.plot(seurat_stage[[stage]])
  graphics.off()
}

HH4

HH6

4ss

8ss

Feature plots

Plot features listed below at each stage

GOI = list("hh6" = c("DLX5", "SIX1", "GATA2", "MSX1", "BMP4", "SIX3", "GBX2", "SOX2", "SOX21"),
            "ss4" = c("SIX1", "EYA2", "CSRNP1", "PAX7", "WNT4", "SIX3", "OLIG2", "SOX2", "SOX21"),
            "ss8" = c("SIX1", "EYA2", "SOX10", "TFAP2A", "GBX2", "SIX3", "OLIG2", "SOX2", "SOX21"))

for(stage in names(GOI)){
  ncol = 3
  pdf(paste0(curr.plot.path, "UMAP_GOI.", stage, ".pdf"), width = ncol*4, height = 5*ceiling(length(genes)/ncol))
  multi.feature.plot(seurat_stage[[stage]], stage.name = stage, n.col = ncol, label = "", gene.list = unlist(GOI[names(GOI) %in% stage]))
  graphics.off()
}

HH4

HH6

4ss

8ss

DotPlots

Change order or clusters for plotting dotplots

levels = list("hh6" = c(5,0,3,2,1,4), "ss4" = c(2,3,1,4,0), "ss8" = c(3,2,1,4,0))
for(stage in names(levels)){
  seurat_stage[[stage]]$seurat_clusters <- factor(seurat_stage[[stage]]$seurat_clusters, levels = unlist(levels[names(levels) %in% stage]))
}

Plot dotplot to identify clusters

for(stage in names(GOI)){
  pdf(paste0(curr.plot.path, "dotplot.", stage, ".pdf"), width = 15, height = 6)
  print(DotPlot(seurat_stage[[stage]], group.by = "seurat_clusters", features = unlist(GOI[names(GOI) %in% stage])) +
          theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)))
  graphics.off()
}

HH4

HH6

4ss

8ss

Make list of clusters to subset

clust.sub = list("hh6" = c(2,1,4), "ss4" = c(0,1,4), "ss8" = c(0,1,4))

---

Subset neural cells from clear seurat data (norm.data.clustfilt.cc)

---

Set plot path

curr.plot.path <- paste0(plot.path, "5_neural_subset/")
dir.create(curr.plot.path)

Get cell IDs from each stage based on clusters to subset

cell.sub = unlist(lapply(names(clust.sub), function(x){
  rownames(seurat_stage[[x]]@meta.data)[seurat_stage[[x]]$seurat_clusters %in% unlist(clust.sub[names(clust.sub) %in% x])]
}))

Subset neural cells

neural.seurat <- subset(norm.data.clustfilt.cc, cells = cell.sub)

Re-run findvariablefeatures and scaling

neural.seurat <- FindVariableFeatures(neural.seurat, selection.method = "vst", nfeatures = 2000)
neural.seurat <- ScaleData(neural.seurat, features = rownames(neural.seurat), vars.to.regress = c("percent.mt", "sex", "S.Score", "G2M.Score"))

saveRDS(neural.seurat, paste0(rds.path, "norm.data.clustfilt.cc.RDS"))

Read in RDS data if needed

# neural.seurat <- readRDS(paste0(rds.path, "neural.seurat.RDS"))

PCA

neural.seurat <- RunPCA(object = neural.seurat, verbose = FALSE)

pdf(paste0(curr.plot.path, "dimHM.pdf"),width=15,height=25)
DimHeatmap(neural.seurat, dims = 1:30, balanced = TRUE, cells = 500)
graphics.off()

pdf(paste0(curr.plot.path, "elbowplot.pdf"),width=12,height=10)
print(ElbowPlot(neural.seurat, ndims = 40))
graphics.off()

pdf(paste0(curr.plot.path, 'UMAP_PCA_comparison.pdf'), width= 20, height= 15)
PCA.level.comparison(neural.seurat, PCA.levels = c(7, 10, 15, 20), cluster_res = 0.5)
graphics.off()

Dimensions heatmap

ElbowPlot

PCA level comparison

Clustering and UMAP

Use PCA=15 as elbow plot is relatively stable across stages

neural.seurat <- FindNeighbors(neural.seurat, dims = 1:20, verbose = FALSE)
neural.seurat <- RunUMAP(neural.seurat, dims = 1:20, verbose = FALSE)

Find optimal cluster resolution

pdf(paste0(curr.plot.path, "clustree.pdf"), width= 25, height= 15, onefile = F)
clust.res(seurat.obj = neural.seurat, by = 0.2)
graphics.off()

Use clustering resolution = 1.2

neural.seurat <- FindClusters(neural.seurat, resolution = 1.2)

UMAP

Plot UMAP for clusters and developmental stage

pdf(paste0(curr.plot.path, "UMAP.pdf"), width=10, height=5)
clust.stage.plot(neural.seurat)
graphics.off()

Cluster QC

Plot QC for each cluster

pdf(paste0(curr.plot.path, "cluster.QC.pdf"), height = 7, width = 18)
QC.plot(neural.seurat)
graphics.off()

DE heatmap

Find differentially expressed genes and plot heatmap of top DE genes for each cluster

markers <- FindAllMarkers(neural.seurat, only.pos = T, logfc.threshold = 0.25)
# Re-order genes in top15 based on desired cluster order in subsequent plot - this orders them in the heatmap in the correct order
top15 <- markers %>% group_by(cluster) %>% top_n(n = 15, wt = avg_logFC)

png(paste0(curr.plot.path, "HM.top15.DE.png"), width=100, height=75, units = "cm", res = 200)
tenx.pheatmap(data = neural.seurat, metadata = c("orig.ident", "seurat_clusters"), used.genes = unique(top15$gene),
              main = "", order.by = "seurat_clusters")
graphics.off()

saveRDS(neural.seurat, paste0(rds.path, "neural.seurat.out.RDS"))